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numericalunits 1.13

A package that lets you define quantities with units, which can then be used in almost any numerical calculation in any programming language. Checks that calculations pass dimensional analysis, performs unit conversions, and defines physical constants.

This package implements units and dimensional analysis in an unconventional
way that has the following unique advantages:

Compatible with everything: Compatible with virtually any numerical
calculation routine, including numpy and scipy, and even including routines
not written in Python! That means, for example, if you have a decades-old
closed-source C routine for numerical integration, you can pass it a
quantity with units of velocity and an integration range with units of
time, and the final answer will magically have units of distance. This
extreme compatibility is possible because if the variable x represents
a quantity with dimensions (like “3.3 kg”), x is actually stored
internally as an ordinary floating-point number. The dimension is
encoded in the value as a multiplicative factor. When two numbers are
multiplied, their dimensions are automatically multiplied, and so on.

Modular and non-intrusive: When you input data, you say what units
they are in. When you display results, you say what units you want to
display them in. These steps are very little trouble, and in fact help you
create nice, self-documenting code. Other than that, you have to do nothing
at all to pass dimensionful quantities into and out of any already-written
programs or routines.

Powerful tool for debugging: Not all calculation mistakes cause
violations of dimensional analysis, but most do–for example, if you
accidentally multiply two lengths instead of adding them, the result will
have the wrong dimension. If you use this package, it will alert you to
these sorts of mistakes.

Zero storage overhead

Zero calculation overhead

These great features come with the disadvantage that the interface is less
slick than other unit packages. If you have a quantity with units, you
cannot directly see what the units are. You are supposed to already know
what the units are, and then the package will tell you whether you made a
mistake. Even worse, you only get alerted to the mistake after running a
calculation all the way through twice.

Therefore the package is not suggested for students exploring how units work.
It is suggested for engineering and science professionals who want to make
their code more self-documenting and self-debugging.

Installation

You can install from PyPI:

pip install numericalunits

Alternatively—since it’s a single module that requires no setup or
compilation—you can download numericalunits.py from PyPI or github and use it directly.

Usage and examples

At the top of the code you’re working on, write:

import numericalunits as nu
nu.reset_units()

Unit errors, like trying to add a length to a mass, will not immediately
announce themselves as unit errors. Instead, you need to run the whole
calculation (including the reset_units() part) twice. If you get the
same final answers both times, then congratulations, all your calculations
are almost guaranteed to pass dimensional analysis! If you get different
answers every time you run, then you made a unit error! It is up to you to
figure out where and what the error is.

To assign a unit to a quantity, multiply by the unit, e.g.
my_length = 100 * mm. (In normal text you would write “100 mm”, but
unfortunately Python does not have “implied multiplication”.)

To express a dimensionful quantity in a certain unit, divide by that unit,
e.g. when you see my_length / cm, you pronounce it “my_length expressed
in cm”.

Example 3: You measured a voltage as a function of the position of dial:
10 volts when the dial is at 1cm, 11 volts when the dial is at 2cm, etc.
etc. Interpolate from this data to get the expected voltage when the dial is
at 41mm, and express the answer in mV.

import numericalunits as nu
nu.reset_units()
(1 * nu.cm) / nu.atm # --> a randomly-varying number
# The answer randomly varies every time you run this, indicating that you
# are violating dimensional analysis.

How it works

A complete set of independent base units (meters, kilograms, seconds,
coulombs, kelvins) are defined as randomly-chosen positive floating-point
numbers. All other units and constants are defined in terms of those. In a
dimensionally-correct calculation, the units all cancel out, so the final
answer is deterministic, not random. In a dimensionally-incorrect
calculations, there will be random factors causing a randomly-varying final
answer.

Included units and constants

Includes a variety of common units, both SI and non-SI, everything from
frequency to magnetic flux. Also includes common physical constants like
Planck’s constant and the speed of light. Browse the source code to see a
complete list. It is very easy to add in any extra units and constants that
were left out.

Notes

Notes on implementation and use

The units should not be reset in the middle of a calculation. They
should be randomly chosen once at the beginning, then carried through
consistently. My advice on how to do that:

For little, self-contained calculations, follow the examples above. Put
reset_units() at the beginning of the calculation, then check for
dimensional errors by re-running the whole calculation (including the
reset_units() part). Note that if you are using from-style imports,
like from numericalunits import cm, you need to put them afterreset_units() in the code.

For more complicated calculations, don’t use reset_units() at all.
Instead, check for dimensional errors by re-running the calculation in a new
Python session. (By “complicated” I mainly mean “involving modules”.)

(Why does this work? Because the first time numericalunits is imported
in a given Python session, reset_units() is run automatically. That
happens only once, so multiple modules can import numericalunits, and
they will all share a random, but self-consistent, set of units.)

(If you want to check for dimensional errors but you really really don’t
want to open a new Python session, you need to reset_units()and
reload every module that has dimensionful variables in its namespace. It’s
not impossible, but it’s annoying and error-prone.)

While debugging a program, it may be annoying to have intermediate values
in the calculation that randomly vary every time you run the program. In
this case, you can use reset_units('SI') instead of the normal
reset_units(). This puts all dimensionful variables in standard (MKS)
SI units: All times are in seconds, all lengths are in meters, all forces
are in newtons, etc. Alternatively, reset_units(123) uses 123 as
the seed for the random-number generator. Obviously, in these modes, you
will not get any indication of dimensional-analysis errors.

There are very rare, strange cases where the final answer does not seem to
randomly vary even though there was a dimensional-analysis violation: For
example, the expression (1 + 1e-50 * cm / atm) fails dimensional
analysis, so if you calculate it the answer is randomly-varying. But, it is
only randomly varying around the 50th decimal point, so the variation is
hidden from view. You would not notice it as an error.

Since units are normal Python float-type numbers, they follow the normal
casting rules. For example, 2 * cm is a python float, not an int.
This is usually what you would want and expect.

You can give a dimension to complex numbers in the same way as real
numbers–for example (2.1e3 + 3.9e4j) * ohm.

Should be compatible with any Python version 2.x or 3.x. If you find bugs,
please tell me by email or
github issue board.

If you get overflows or underflows, you can edit the unit initializations.
For example, the package sets the meter to a numerical value between 0.1
and 10. Therefore, if you’re doing molecular simulation, most lengths you
use will be tiny numbers. You should probably set the meter instead to be
between, say, 1e8 and 1e10.

Some numerical routines use a default absolute tolerance, rather than
relative tolerance, to decide convergence. This can cause the calculation
result to randomly vary even though there is no dimensional analysis error.
When this happens, you should set the absolute tolerance to a value with the
appropriate units. Alternatively, you can scale the data before running the
algorithm and scale it back afterwards. Maybe this sounds like a hassle, but
it’s actually a benefit: If your final result is very sensitive to some
numerical tolerance setting, then you really want to be aware of that.

Notes on unit definitions

For electromagnetism, all units are intended for use in SI formulas. If
you plug them into cgs-gaussian electromagnetism formulas, or cgs-esu
electromagnetism formulas, etc., you will get nonsense results.

The package does not keep track of “radians” as an independent unit
assigned a random number. The reason is that the “radians” factor does not
always neatly cancel out of formulas.

The package does not keep track of “moles” as an independent unit assigned
a random number; instead mol is just a pure number (~6e23), like you
would say “dozen”=12. That means: (1) gram/mol is exactly the same as amu,
and Boltzmann constant is exactly the same as the ideal gas constant, and so
on. (2) You should rarely need to use Avogadro’s number NA – it is just a
synonym of mol (NA = mol ~ 6e23). Here are a few examples using moles:

The package cannot convert temperatures between Fahrenheit, Celsius, and
kelvin. The reason is that these scales have different zeros, so the units
cannot be treated as multiplicative factors. It is, however, possible to
convert temperature intervals, via the units degCinterval (which is a
synonym of kelvin, K) and degFinterval.